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A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data
The financial risk not only affects the development of the company itself, but also affects the economic development of the whole society; therefore, the financial risk assessment of company is an important part. At present, numerous methods of financial risk assessment have been researched by schol...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6306178/ https://www.ncbi.nlm.nih.gov/pubmed/30586437 http://dx.doi.org/10.1371/journal.pone.0208166 |
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author | Li, Dan-Ping Cheng, Si-Jie Cheng, Peng-Fei Wang, Jian-Qiang Zhang, Hong-Yu |
author_facet | Li, Dan-Ping Cheng, Si-Jie Cheng, Peng-Fei Wang, Jian-Qiang Zhang, Hong-Yu |
author_sort | Li, Dan-Ping |
collection | PubMed |
description | The financial risk not only affects the development of the company itself, but also affects the economic development of the whole society; therefore, the financial risk assessment of company is an important part. At present, numerous methods of financial risk assessment have been researched by scholars. However, most of the extant methods neither integrated fuzzy sets with quantitative analysis, nor took into account the historical data of the past few years. To settle these defects, this paper proposes a novel financial risk assessment model for companies based on heterogeneous multiple-criteria decision-making (MCDM) and historical data. Subjective and objective indexes are comprehensively taken into consideration in the financial risk assessment index system of the model, which combines fuzzy theory with quantitative data analysis. Moreover, the assessment information obtained from historical financial information of company, credit rating agency and decision makers, including crisp numbers, triangular fuzzy numbers and neutrosophic numbers. Furthermore, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to determine the ranking order of companies according to their financial risk. Finally, an empirical study of financial risk assessment for companies is conducted, and the results of comparative analysis and sensitivity analysis suggest that the proposed model can effectively and reliably obtain the company with the lowest financial risk. |
format | Online Article Text |
id | pubmed-6306178 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-63061782019-01-08 A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data Li, Dan-Ping Cheng, Si-Jie Cheng, Peng-Fei Wang, Jian-Qiang Zhang, Hong-Yu PLoS One Research Article The financial risk not only affects the development of the company itself, but also affects the economic development of the whole society; therefore, the financial risk assessment of company is an important part. At present, numerous methods of financial risk assessment have been researched by scholars. However, most of the extant methods neither integrated fuzzy sets with quantitative analysis, nor took into account the historical data of the past few years. To settle these defects, this paper proposes a novel financial risk assessment model for companies based on heterogeneous multiple-criteria decision-making (MCDM) and historical data. Subjective and objective indexes are comprehensively taken into consideration in the financial risk assessment index system of the model, which combines fuzzy theory with quantitative data analysis. Moreover, the assessment information obtained from historical financial information of company, credit rating agency and decision makers, including crisp numbers, triangular fuzzy numbers and neutrosophic numbers. Furthermore, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to determine the ranking order of companies according to their financial risk. Finally, an empirical study of financial risk assessment for companies is conducted, and the results of comparative analysis and sensitivity analysis suggest that the proposed model can effectively and reliably obtain the company with the lowest financial risk. Public Library of Science 2018-12-26 /pmc/articles/PMC6306178/ /pubmed/30586437 http://dx.doi.org/10.1371/journal.pone.0208166 Text en © 2018 Li et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Li, Dan-Ping Cheng, Si-Jie Cheng, Peng-Fei Wang, Jian-Qiang Zhang, Hong-Yu A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data |
title | A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data |
title_full | A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data |
title_fullStr | A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data |
title_full_unstemmed | A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data |
title_short | A novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data |
title_sort | novel financial risk assessment model for companies based on heterogeneous information and aggregated historical data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6306178/ https://www.ncbi.nlm.nih.gov/pubmed/30586437 http://dx.doi.org/10.1371/journal.pone.0208166 |
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